Assignment 9
Dipesh Poudel
1/8/2022
Assignment 9: Association Rule Mining
Instructions Use the “AdultUCI” data available in the “arules” package and do as follows in R Script.
1. Attach the AdultUCI data in R
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writedata(AdultUCI)- Check the class, structure, dimension, head and tail of the attached data and write interpretations
Checking the Class of the dataset
class(AdultUCI)## [1] "data.frame"Checking the Structure of the dataset
str(AdultUCI)## 'data.frame': 48842 obs. of 15 variables:
## $ age : int 39 50 38 53 28 37 49 52 31 42 ...
## $ workclass : Factor w/ 8 levels "Federal-gov",..: 7 6 4 4 4 4 4 6 4 4 ...
## $ fnlwgt : int 77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...
## $ education : Ord.factor w/ 16 levels "Preschool"<"1st-4th"<..: 14 14 9 7 14 15 5 9 15 14 ...
## $ education-num : int 13 13 9 7 13 14 5 9 14 13 ...
## $ marital-status: Factor w/ 7 levels "Divorced","Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
## $ occupation : Factor w/ 14 levels "Adm-clerical",..: 1 4 6 6 10 4 8 4 10 4 ...
## $ relationship : Factor w/ 6 levels "Husband","Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...
## $ race : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 1 1 1 2 1 2 ...
## $ capital-gain : int 2174 0 0 0 0 0 0 0 14084 5178 ...
## $ capital-loss : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hours-per-week: int 40 13 40 40 40 40 16 45 50 40 ...
## $ native-country: Factor w/ 41 levels "Cambodia","Canada",..: 39 39 39 39 5 39 23 39 39 39 ...
## $ income : Ord.factor w/ 2 levels "small"<"large": 1 1 1 1 1 1 1 2 2 2 ...Checking the Dimesions of the dataset
dim(AdultUCI)## [1] 48842 15head(AdultUCI)## age workclass fnlwgt education education-num marital-status
## 1 39 State-gov 77516 Bachelors 13 Never-married
## 2 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse
## 3 38 Private 215646 HS-grad 9 Divorced
## 4 53 Private 234721 11th 7 Married-civ-spouse
## 5 28 Private 338409 Bachelors 13 Married-civ-spouse
## 6 37 Private 284582 Masters 14 Married-civ-spouse
## occupation relationship race sex capital-gain capital-loss
## 1 Adm-clerical Not-in-family White Male 2174 0
## 2 Exec-managerial Husband White Male 0 0
## 3 Handlers-cleaners Not-in-family White Male 0 0
## 4 Handlers-cleaners Husband Black Male 0 0
## 5 Prof-specialty Wife Black Female 0 0
## 6 Exec-managerial Wife White Female 0 0
## hours-per-week native-country income
## 1 40 United-States small
## 2 13 United-States small
## 3 40 United-States small
## 4 40 United-States small
## 5 40 Cuba small
## 6 40 United-States smalltail(AdultUCI)## age workclass fnlwgt education education-num marital-status
## 48837 33 Private 245211 Bachelors 13 Never-married
## 48838 39 Private 215419 Bachelors 13 Divorced
## 48839 64 <NA> 321403 HS-grad 9 Widowed
## 48840 38 Private 374983 Bachelors 13 Married-civ-spouse
## 48841 44 Private 83891 Bachelors 13 Divorced
## 48842 35 Self-emp-inc 182148 Bachelors 13 Married-civ-spouse
## occupation relationship race sex capital-gain
## 48837 Prof-specialty Own-child White Male 0
## 48838 Prof-specialty Not-in-family White Female 0
## 48839 <NA> Other-relative Black Male 0
## 48840 Prof-specialty Husband White Male 0
## 48841 Adm-clerical Own-child Asian-Pac-Islander Male 5455
## 48842 Exec-managerial Husband White Male 0
## capital-loss hours-per-week native-country income
## 48837 0 40 United-States <NA>
## 48838 0 36 United-States <NA>
## 48839 0 40 United-States <NA>
## 48840 0 50 United-States <NA>
## 48841 0 40 United-States <NA>
## 48842 0 60 United-States <NA>There are 48842 rows(observations) and 15 columns (variables) in the dataset. Variables age fnlwgt,education-num,capital-gain,capital-loss,hours-per-week have integers values. Variables workclass, education, marital-status, occupation, race,sex, native-country, income and relationship are factors out of them income and education are ordered factors.
3. Remove “fnlwgt” and “education-num” variables from the attached data and explain the logic you have used here
# Changing dash(-) sign in column names to underscore(_)
names(AdultUCI)<-gsub("-","_",names(AdultUCI))
# Removing the said variables
AdultUCI<-subset(AdultUCI,select=c(-education_num,-fnlwgt))First of all the minus(-) sign in column name was replaced with underscore(_) sign since r interprets the minus sign as a operator. Then the columns fnlwgt and education-num variable (changed to education_num) was removed using subset command.
4. Convert “age” as ordered factor variables with cuts at 15, 25, 45, 65 and 100 and label it as “Young”, “Middle-aged”, “Senior” and “Old”
age_labels<-c("Young","Middle-aged","Senior","old")
AdultUCI$age<-cut(AdultUCI$age,breaks = c(15,25,45,65,100),labels = age_labels)
AdultUCI$age<-factor(AdultUCI$age,ordered = T,labels = age_labels)
str(AdultUCI$age)## Ord.factor w/ 4 levels "Young"<"Middle-aged"<..: 2 3 2 3 2 2 3 3 2 2 ...5. Convert the “hours-per-week” as ordered factor variable with cuts at 0, 25, 40, 60, 168 and label it as “Part-time”, “Full-time”, “Over-time” and “Workaholic”
hours_labels<-c("Part-time","Full-time","Over-time","Workaholic")
AdultUCI$hours_per_week<-cut(AdultUCI$hours_per_week,breaks = c(0,25,40,60,168),labels = hours_labels)
AdultUCI$hours_per_week<-factor(AdultUCI$hours_per_week,ordered = T,labels = hours_labels)
str(AdultUCI$hours_per_week)## Ord.factor w/ 4 levels "Part-time"<"Full-time"<..: 2 1 2 2 2 2 1 3 3 2 ...6. Convert the “capital-gain” as ordered factor variable with cuts at –Inf, 0, median and Inf and label it as “None”, “Low” and “High”
capital_gain_labels<-c("None","Low","High")
AdultUCI$capital_gain<-cut(AdultUCI$capital_gain,breaks = c(-Inf,0,median(AdultUCI[AdultUCI$capital_gain>0,]$capital_gain),Inf),labels = capital_gain_labels)
AdultUCI$capital_gain<-factor(AdultUCI$capital_gain,ordered = T,labels = capital_gain_labels)
str(AdultUCI$capital_gain)## Ord.factor w/ 3 levels "None"<"Low"<"High": 2 1 1 1 1 1 1 1 3 2 ...7. Convert the “capital-loss” as ordered factor variable with cuts at –Inf, 0, median and Inf and label it as “None”, “Low” and “High”
capital_loss_labels<-c("None","Low","High")
AdultUCI$capital_loss<-cut(AdultUCI$capital_loss,breaks = c(-Inf,0,median(AdultUCI[AdultUCI$capital_loss>0,]$capital_loss),Inf),labels = capital_loss_labels)
AdultUCI$capital_loss<-factor(AdultUCI$capital_loss,ordered = T,labels = capital_loss_labels)
str(AdultUCI$capital_loss)## Ord.factor w/ 3 levels "None"<"Low"<"High": 1 1 1 1 1 1 1 1 1 1 ...8. Create transactions of AdultUCI data as “Adult” and check it with “Adult” command
Adult<-transactions(AdultUCI)
Adult## transactions in sparse format with
## 48842 transactions (rows) and
## 115 items (columns)9. et summary of the “Adult” and interpret it critically
summary(Adult)## transactions as itemMatrix in sparse format with
## 48842 rows (elements/itemsets/transactions) and
## 115 columns (items) and a density of 0.1089939
##
## most frequent items:
## capital_loss=None capital_gain=None
## 46560 44807
## native_country=United-States race=White
## 43832 41762
## workclass=Private (Other)
## 33906 401333
##
## element (itemset/transaction) length distribution:
## sizes
## 9 10 11 12 13
## 19 971 2067 15623 30162
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.00 12.00 13.00 12.53 13.00 13.00
##
## includes extended item information - examples:
## labels variables levels
## 1 age=Young age Young
## 2 age=Middle-aged age Middle-aged
## 3 age=Senior age Senior
##
## includes extended transaction information - examples:
## transactionID
## 1 1
## 2 2
## 3 3From Summary: We have 48842 rows (elements/itemsets/transactions) and 115 columns (items). The most frequent most items have capital_loss=None, capital_gain=None.
10. Inspect head and tail of the “Adult” and interpret them carefully
inspect(head(Adult))## items transactionID
## [1] {age=Middle-aged,
## workclass=State-gov,
## education=Bachelors,
## marital_status=Never-married,
## occupation=Adm-clerical,
## relationship=Not-in-family,
## race=White,
## sex=Male,
## capital_gain=Low,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States,
## income=small} 1
## [2] {age=Senior,
## workclass=Self-emp-not-inc,
## education=Bachelors,
## marital_status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Husband,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Part-time,
## native_country=United-States,
## income=small} 2
## [3] {age=Middle-aged,
## workclass=Private,
## education=HS-grad,
## marital_status=Divorced,
## occupation=Handlers-cleaners,
## relationship=Not-in-family,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States,
## income=small} 3
## [4] {age=Senior,
## workclass=Private,
## education=11th,
## marital_status=Married-civ-spouse,
## occupation=Handlers-cleaners,
## relationship=Husband,
## race=Black,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States,
## income=small} 4
## [5] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Married-civ-spouse,
## occupation=Prof-specialty,
## relationship=Wife,
## race=Black,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=Cuba,
## income=small} 5
## [6] {age=Middle-aged,
## workclass=Private,
## education=Masters,
## marital_status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Wife,
## race=White,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States,
## income=small} 6inspect(tail(Adult))## items transactionID
## [1] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Never-married,
## occupation=Prof-specialty,
## relationship=Own-child,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States} 48837
## [2] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Divorced,
## occupation=Prof-specialty,
## relationship=Not-in-family,
## race=White,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States} 48838
## [3] {age=Senior,
## education=HS-grad,
## marital_status=Widowed,
## relationship=Other-relative,
## race=Black,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States} 48839
## [4] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Married-civ-spouse,
## occupation=Prof-specialty,
## relationship=Husband,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Over-time,
## native_country=United-States} 48840
## [5] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Divorced,
## occupation=Adm-clerical,
## relationship=Own-child,
## race=Asian-Pac-Islander,
## sex=Male,
## capital_gain=Low,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States} 48841
## [6] {age=Middle-aged,
## workclass=Self-emp-inc,
## education=Bachelors,
## marital_status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Husband,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Over-time,
## native_country=United-States} 48842Each row is given a transactionID and values of each row is converted into a list of items in a transaction.
11 Create absolute and relative item frequency plot and color it with RColorBrewer package
library(RColorBrewer)
palette = brewer.pal(10,'RdYlBu');
# Absolute Frequency Plot
itemFrequencyPlot(Adult,
type="absolute",
topN=10,
col=palette,
main="Absolute Frequency Plot",
xlab="Items"
)
itemFrequencyPlot(Adult,
type="relative",
topN=10,
col=palette,
main="Relative Frequency Plot",
xlab="Items")
12. Create an apriori rule as “association.rule” with support = 1%, confidence = 80% and maximum length of the rule as 10. Get summary of this rule and interpret it carefully.
association.rule<-apriori(Adult,
parameter = list(supp=0.01,conf=0.8,maxlen=10,target= "rules")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 488
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[115 item(s), 48842 transaction(s)] done [0.05s].
## sorting and recoding items ... [67 item(s)] done [0.01s].
## creating transaction tree ... done [0.04s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen
## = 10, : Mining stopped (maxlen reached). Only patterns up to a length of 10
## returned!## done [0.90s].
## writing ... [197371 rule(s)] done [0.04s].
## creating S4 object ... done [0.06s].summary(association.rule)## set of 197371 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4 5 6 7 8 9 10
## 4 266 3303 15219 37015 53616 48402 27754 9827 1965
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 6.000 6.318 7.000 10.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01001 Min. :0.8000 Min. :0.01001 Min. : 0.8677
## 1st Qu.:0.01251 1st Qu.:0.8953 1st Qu.:0.01353 1st Qu.: 1.0059
## Median :0.01708 Median :0.9372 Median :0.01847 Median : 1.0398
## Mean :0.02726 Mean :0.9283 Mean :0.02949 Mean : 1.2899
## 3rd Qu.:0.02766 3rd Qu.:0.9669 3rd Qu.:0.02995 3rd Qu.: 1.2160
## Max. :0.95328 Max. :1.0000 Max. :1.00000 Max. :20.6826
## count
## Min. : 489
## 1st Qu.: 611
## Median : 834
## Mean : 1331
## 3rd Qu.: 1351
## Max. :46560
##
## mining info:
## data ntransactions support confidence
## Adult 48842 0.01 0.8
## call
## apriori(data = Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen = 10, target = "rules"))We got 197371 rules. We can also see number of rules with different numbers of items.
13. Inspect the first 10 rules and interpret it critically.
inspect(head(association.rule,10))## lhs rhs support confidence coverage lift count
## [1] {} => {race=White} 0.85504279 0.8550428 1.00000000 1.0000000 41762
## [2] {} => {native_country=United-States} 0.89742435 0.8974243 1.00000000 1.0000000 43832
## [3] {} => {capital_gain=None} 0.91738668 0.9173867 1.00000000 1.0000000 44807
## [4] {} => {capital_loss=None} 0.95327792 0.9532779 1.00000000 1.0000000 46560
## [5] {education=5th-6th} => {capital_loss=None} 0.01009377 0.9685658 0.01042136 1.0160372 493
## [6] {education=Doctorate} => {race=White} 0.01076942 0.8855219 0.01216166 1.0356463 526
## [7] {education=Doctorate} => {capital_loss=None} 0.01076942 0.8855219 0.01216166 0.9289231 526
## [8] {marital_status=Married-spouse-absent} => {capital_gain=None} 0.01218214 0.9474522 0.01285779 1.0327730 595
## [9] {marital_status=Married-spouse-absent} => {capital_loss=None} 0.01240735 0.9649682 0.01285779 1.0122632 606
## [10] {education=12th} => {native_country=United-States} 0.01140412 0.8477930 0.01345154 0.9446958 55714. Remove the empty rules from the “association.rule” and inspect the first 10 rules with interpretations.
association.rule<-apriori(Adult,
parameter = list(supp=0.01,conf=0.8,maxlen=10,minlen=2,target= "rules")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 488
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[115 item(s), 48842 transaction(s)] done [0.06s].
## sorting and recoding items ... [67 item(s)] done [0.01s].
## creating transaction tree ... done [0.04s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen
## = 10, : Mining stopped (maxlen reached). Only patterns up to a length of 10
## returned!## done [0.88s].
## writing ... [197367 rule(s)] done [0.04s].
## creating S4 object ... done [0.06s].inspect(head(association.rule,10))## lhs rhs support confidence coverage lift count
## [1] {education=5th-6th} => {capital_loss=None} 0.01009377 0.9685658 0.01042136 1.0160372 493
## [2] {education=Doctorate} => {race=White} 0.01076942 0.8855219 0.01216166 1.0356463 526
## [3] {education=Doctorate} => {capital_loss=None} 0.01076942 0.8855219 0.01216166 0.9289231 526
## [4] {marital_status=Married-spouse-absent} => {capital_gain=None} 0.01218214 0.9474522 0.01285779 1.0327730 595
## [5] {marital_status=Married-spouse-absent} => {capital_loss=None} 0.01240735 0.9649682 0.01285779 1.0122632 606
## [6] {education=12th} => {native_country=United-States} 0.01140412 0.8477930 0.01345154 0.9446958 557
## [7] {education=12th} => {capital_gain=None} 0.01289873 0.9589041 0.01345154 1.0452562 630
## [8] {education=12th} => {capital_loss=None} 0.01322632 0.9832572 0.01345154 1.0314487 646
## [9] {education=9th} => {race=White} 0.01250973 0.8082011 0.01547848 0.9452171 611
## [10] {education=9th} => {capital_gain=None} 0.01457762 0.9417989 0.01547848 1.0266107 71215. Create a new rule as “capital.gain.rhs.rule” with “capital-gain=None” in the RHS with support of 1%, confidence of 80%, maximum length of 10 and minimum length of 2.
capital.gain.rhs.rule<-apriori(Adult,
parameter=list(supp=0.01,conf=0.8,maxlen=10,minlen=2),
appearance=list(default="lhs",rhs="capital_gain=None")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 488
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[115 item(s), 48842 transaction(s)] done [0.05s].
## sorting and recoding items ... [67 item(s)] done [0.01s].
## creating transaction tree ... done [0.04s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen
## = 10, : Mining stopped (maxlen reached). Only patterns up to a length of 10
## returned!## done [0.88s].
## writing ... [35433 rule(s)] done [0.01s].
## creating S4 object ... done [0.02s].16. Get summary of this rule and interpret it critically.
summary(capital.gain.rhs.rule)## set of 35433 rules
##
## rule length distribution (lhs + rhs):sizes
## 2 3 4 5 6 7 8 9 10
## 60 706 3062 7110 9790 8377 4537 1508 283
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 5.000 6.000 6.212 7.000 10.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01001 Min. :0.8000 Min. :0.01009 Min. :0.8720
## 1st Qu.:0.01265 1st Qu.:0.9015 1st Qu.:0.01359 1st Qu.:0.9827
## Median :0.01744 Median :0.9453 Median :0.01882 Median :1.0304
## Mean :0.02819 Mean :0.9287 Mean :0.03051 Mean :1.0124
## 3rd Qu.:0.02864 3rd Qu.:0.9654 3rd Qu.:0.03092 3rd Qu.:1.0523
## Max. :0.87066 Max. :1.0000 Max. :0.95328 Max. :1.0901
## count
## Min. : 489
## 1st Qu.: 618
## Median : 852
## Mean : 1377
## 3rd Qu.: 1399
## Max. :42525
##
## mining info:
## data ntransactions support confidence
## Adult 48842 0.01 0.8
## call
## apriori(data = Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen = 10, minlen = 2), appearance = list(default = "lhs", rhs = "capital_gain=None"))We got set of 35433 rules.
17. Create a new rule as “hours.per.week.ft.rule” with “hour-per-week=Full-time” in the RHS with support of 1%, confidence of 80%, maximum length of 10 and minimum length of 2.
hours.per.week.ft.rule<-apriori(Adult,
parameter=list(supp=0.01,conf=0.8,maxlen=10,minlen=2),
appearance=list(default="lhs",rhs="hours_per_week=Full-time")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 488
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[115 item(s), 48842 transaction(s)] done [0.05s].
## sorting and recoding items ... [67 item(s)] done [0.01s].
## creating transaction tree ... done [0.04s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen
## = 10, : Mining stopped (maxlen reached). Only patterns up to a length of 10
## returned!## done [0.89s].
## writing ... [159 rule(s)] done [0.00s].
## creating S4 object ... done [0.01s].18. Get summary of this rule and interpret it critically.
summary(hours.per.week.ft.rule)## set of 159 rules
##
## rule length distribution (lhs + rhs):sizes
## 3 4 5 6 7 8
## 3 16 48 58 29 5
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.686 6.000 8.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01001 Min. :0.8000 Min. :0.01216 Min. :1.367
## 1st Qu.:0.01066 1st Qu.:0.8047 1st Qu.:0.01318 1st Qu.:1.375
## Median :0.01179 Median :0.8086 Median :0.01456 Median :1.382
## Mean :0.01237 Mean :0.8089 Mean :0.01530 Mean :1.383
## 3rd Qu.:0.01331 3rd Qu.:0.8129 3rd Qu.:0.01654 3rd Qu.:1.389
## Max. :0.01992 Max. :0.8266 Max. :0.02471 Max. :1.413
## count
## Min. :489.0
## 1st Qu.:520.5
## Median :576.0
## Mean :604.4
## 3rd Qu.:650.0
## Max. :973.0
##
## mining info:
## data ntransactions support confidence
## Adult 48842 0.01 0.8
## call
## apriori(data = Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen = 10, minlen = 2), appearance = list(default = "lhs", rhs = "hours_per_week=Full-time"))19. Get new rule of “hours.per.week.ft.rule” as “conf.sort.rule” by sorting “hours.per.week.ft.rule” in descending order by “confidence” and inspect the head and tail rules with critical interpretation.
conf.sort.rule<-sort(hours.per.week.ft.rule,by="confidence",decreasing = TRUE)inspect(head(conf.sort.rule))## lhs rhs support confidence coverage lift count
## [1] {age=Middle-aged,
## occupation=Adm-clerical,
## relationship=Unmarried,
## sex=Female,
## capital_gain=None} => {hours_per_week=Full-time} 0.01005282 0.8265993 0.01216166 1.412771 491
## [2] {age=Middle-aged,
## occupation=Adm-clerical,
## relationship=Unmarried,
## capital_gain=None} => {hours_per_week=Full-time} 0.01066705 0.8243671 0.01293968 1.408956 521
## [3] {age=Middle-aged,
## occupation=Adm-clerical,
## relationship=Unmarried,
## capital_gain=None,
## capital_loss=None} => {hours_per_week=Full-time} 0.01042136 0.8236246 0.01265304 1.407687 509
## [4] {age=Middle-aged,
## relationship=Unmarried,
## race=Black,
## sex=Female,
## capital_gain=None} => {hours_per_week=Full-time} 0.01029851 0.8218954 0.01253020 1.404732 503
## [5] {age=Middle-aged,
## workclass=Private,
## education=HS-grad,
## race=Black,
## capital_gain=None,
## capital_loss=None} => {hours_per_week=Full-time} 0.01148602 0.8201754 0.01400434 1.401792 561
## [6] {age=Middle-aged,
## education=HS-grad,
## occupation=Adm-clerical,
## sex=Female,
## capital_gain=None,
## capital_loss=None} => {hours_per_week=Full-time} 0.01031899 0.8195122 0.01259162 1.400658 504inspect(tail(conf.sort.rule))## lhs rhs support confidence coverage lift count
## [1] {occupation=Adm-clerical,
## relationship=Unmarried} => {hours_per_week=Full-time} 0.01756685 0.8003731 0.02194832 1.367947 858
## [2] {workclass=Private,
## occupation=Adm-clerical,
## relationship=Unmarried,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## native_country=United-States} => {hours_per_week=Full-time} 0.01033946 0.8003170 0.01291921 1.367851 505
## [3] {occupation=Adm-clerical,
## relationship=Unmarried,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## income=small} => {hours_per_week=Full-time} 0.01042136 0.8003145 0.01302158 1.367847 509
## [4] {occupation=Machine-op-inspct,
## sex=Female,
## capital_gain=None,
## native_country=United-States} => {hours_per_week=Full-time} 0.01031899 0.8000000 0.01289873 1.367309 504
## [5] {occupation=Machine-op-inspct,
## sex=Female,
## capital_loss=None,
## native_country=United-States} => {hours_per_week=Full-time} 0.01040088 0.8000000 0.01300111 1.367309 508
## [6] {workclass=Private,
## education=HS-grad,
## race=Black,
## capital_gain=None,
## native_country=United-States,
## income=small} => {hours_per_week=Full-time} 0.01171123 0.8000000 0.01463904 1.367309 572- Plot the “hours.per.week.ft.rule” with arulesViz package with plot, plot with “two-key plot”, engine=”plotly”, method=graph & engine=htmlwidget and parallel coordinate plot and interpret each graph carefully.
library(arulesViz)
library(plotly)## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot(hours.per.week.ft.rule)## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(hours.per.week.ft.rule,method = "two-key plot")## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(hours.per.week.ft.rule,engine = "plotly")## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.plot(hours.per.week.ft.rule,engine = "htmlwidget",method = "graph")## Warning: Too many rules supplied. Only plotting the best 100 using
## 'lift' (change control parameter max if needed).plot(hours.per.week.ft.rule,method = "paracoord")